Yeah, I caught the tail end of that. But it turns out that there are
just a lot of advantages to centralization. Once the network is really in place,
and you have big parallel computers that hold the data, this is the thing that's
going to make the home robot practical. Because, if you figure how big a
computer you need for a home robot, it is quite substantial. You want it to be
able to hook up your VCR to your piano, and to do things like that that require
a lot of specialized knowledge. It's much more practical if you imagine that
robot with just a little cellular phone to call up to some big database. Because
most of the time the home robot is just moving from A to B and it can be done
with a 4-bit microprocessor. But then occasionally it needs to process a picture
and make some big decision like whether to throw away the dollar bill on the
floor it runs across it while it's vacuuming. And that's exactly the point where
it just wants to be able to ask for help from some big computational facility.
Of course, you'll get charged an extra penny at the end of the month for the
computation.

AK:

I was thinking about ecological computing. When I was working with
computers in the late '60s, all of the computer power on Earth could fit into a
bacterium. The bacterium is only 1/500th of a mammalian cell, and we have 10
trillion of those cells in our bodies. Nothing that we have fashioned directly
is even close to that in power. Pretty soon we're going to have to grow
software, and we should start learning how to do that. We should have software
that won't break when something is wrong with it. As a friend of mine once said,
if you try to make a Boeing 747 six inches longer, you have a problem; but a
baby gets six inches longer ten or more times during its life, and you never
have to take it down for maintenance.

DH:

There are a couple of things that are going to get us into that
ecological computing. If you look at the way that we design software right now,
we basically use the same methods that we used for, say, designing a motorcycle.
Engineering has one technique, which is that you break a problem into parts,
then you define the interactions between those parts, and reapply it to the
whole. So, all you can build with engineering are these nice hierarchical things
that have good, well-defined interactions.

But if you look at a biological organism, it's a very different structure. You
end up with systems that are infinitely more resilient. As you say, they can
grow by 10 percent and it doesn't matter much. People's minds, which are surely
very complicated compared to any software program, don't crash. When I first
came into the MIT Artificial Intelligence Lab, it was the during golden days
when language programs were sort of working and it looked like if you just kept
on heading in that same direction then you could just engineer something that
thought. But what happened was, we sort of reached a wall where things became
more fragile and more difficult to change as they got more complex, and in fact
we never really got much beyond that point. I mean, the state of natural
language understanding today is not a whole lot advanced in terms of performance
above what it was back then.

Now, you could conclude from that that artificial intelligence is just an
impossible task. Marvin [Minsky], who still imagines engineering AI, certainly
has come to the conclusion that the brain is a very complex kludge. So you might
conclude that we can never build one. But you can also conclude that it's simply
the techniques we're using to approach AI that just aren't powerful enough.

AK:

Well, the problem is that nobody knows how to do it the other way.
But that doesn't mean you shouldn't try it.

DH:

I think another way is going to be the only way it's possible. If
we're ever going to make a thinking machine, we're going to have to face the
problem of being able to build things that are more complex than we can
understand. That means we have to build things by some method other than
engineering them. And the only candidate that I'm aware of for that is
biological evolution. But the problem is, as soon as you start doing that, you
start realizing that the story that you were told in school about biological
evolution is way too simple.